A region of interest (ROI) is subset of image pixels that are identified for a particular purpose. This concept is commonly used in image and vision related applications. Normally, several objects, and their locations in the image, are needed from a single scene. For example, in surveillance systems, the system typically concentrates on several specific subjects, such as vehicle license plates, faces, etc., at the same time.
Many ROI extraction methods have been proposed. Recently, many machine learning approaches have been proposed, including Support Vector Machine (SVM), Adaboost, and Convolutional Neural Network (CNN). However, there is no general method that can detect all types of objects with the same parameters. A common and direct method, called ‘Simultaneous Inference,’ applies different object detectors (using either different methods or the same method with different parameters) on the same frame to find all required ROIs from one frame at a time, which requires long processing times, large power consumption, and high output transfer bandwidth.